Colorization To Show Contribution of Different Camera Modalities

ABSTRACT

Techniques for generating an enhanced image. A first image is generated using a camera of a first modality, and a second image is generated using a camera of a second modality. Pixels that are common between the two images are identified. An alpha map is generated. The alpha map reflects edge detection weights that are computed for the common pixels based on saliency values. A determination is made as to how much texture from the images to use to generate an enhanced image. This determination is based on the edge detection weights included within the alpha map. Based on the edge detection weights, textures are merged from the common pixels to generate the enhanced image. Color is also added to the enhanced image, where the color reflects an additional property (e.g., the texture source for the pixel) that is associated with one or both of the images.

BACKGROUND

Mixed-reality (MR) systems, including virtual-reality (VR) andaugmented-reality (AR) systems, have received significant attentionbecause of their ability to create truly unique experiences for theirusers. For reference, conventional VR systems create completelyimmersive experiences by restricting their users' views to only virtualenvironments. This is often achieved through the use of a head-mounteddevice (HMD) that completely blocks any view of the real world. As aresult, a user is entirely immersed within the virtual environment. Incontrast, conventional AR systems create an augmented-reality experienceby visually presenting virtual objects that are placed in or thatinteract with the real world.

As used herein, VR and AR systems are described and referencedinterchangeably. Unless stated otherwise, the descriptions herein applyequally to all types of MR systems, which (as detailed above) include ARsystems, VR reality systems, and/or any other similar system capable ofdisplaying virtual content. Use of the term “HMD” can also refer to a MRsystem.

A MR system can employ different types of cameras (aka “modalities”) inorder to display content to users, such as in the form of a passthroughimage. A passthrough image or view can aid users in avoidingdisorientation and/or safety hazards when transitioning into and/ornavigating within a MR environment. A MR system can present viewscaptured by cameras in a variety of ways. The process of using imagescaptured by world-facing cameras to provide views of a real-worldenvironment provides many advantages. Despite the current benefitsprovided by passthrough images, there are additional benefits that maybe achieved by improving the processes by which passthrough images aregenerated, especially when multiple different cameras are involved.Accordingly, it is desirable to further improve the benefits provided bypassthrough image generation techniques.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one exemplary technology area where some embodimentsdescribed herein may be practiced.

BRIEF SUMMARY

Embodiments disclosed herein relate to systems, devices (e.g., wearabledevices, head mounted devices, hardware storage devices, etc.), andmethods for generating an enhanced image, where the enhanced imagereflects pixel content obtained from cameras of different modalities aswell as contextual content representative of a particular property(e.g., a source that provided texture for that particular pixel).

Some embodiments generate a first image of an environment using a firstcamera of a first modality (e.g., perhaps a thermal camera). A secondimage of the environment is generated using a second camera of a secondmodality (e.g., perhaps a low light camera). The embodiments identifypixels that are common between the first and second images. An alpha mapis then generated, where the alpha map reflects edge detection weightsthat have been computed for each one of the common pixels based on afirst saliency generated for the first image and a second saliencygenerated for the second image. The embodiments determine how muchtexture from the first and/or the second image to use in order togenerate an enhanced image. This determining process is based on theedge detection weights included within the alpha map. Based on thoseweights, textures are merged from the common pixels included in thefirst and second images to generate the enhanced image. Color is alsoadded to the enhanced image, where the color reflects an additionalproperty that is associated with one or both of the first or secondimage.

Some embodiments combine pixel information from multiple differentimages into a single colorized enhanced image. Specifically, someembodiments obtain a first image and a second image of an environment.Those images are then used to generate a colorized enhanced image. Togenerate the colorized enhanced image, the embodiments use pixelinformation from the first image to populate pixel intensity informationfor the colorized enhanced image. Additionally, the embodiments usepixel information from the second image to determine a huecharacteristic of the colorized enhanced image.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of the teachings herein. Features andadvantages of the invention may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. Features of the present invention will become more fullyapparent from the following description and appended claims, or may belearned by the practice of the invention as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof the subject matter briefly described above will be rendered byreference to specific embodiments which are illustrated in the appendeddrawings. Understanding that these drawings depict only typicalembodiments and are not therefore to be considered to be limiting inscope, embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example head mounted device (HMD).

FIG. 2 illustrates how an HMD can include various different cameras.

FIG. 3 illustrates how an HMD may be used to scan an environment togenerate different images.

FIG. 4 illustrates how different images can include common content andhow each pixel in the image can be assigned a texture value.

FIG. 5 illustrates how variations in texture can be used to determine asaliency of an image, where the saliency reflects detected edges.

FIG. 6 illustrates the results of performing edge detection on an image.

FIG. 7 illustrates different techniques for performing edge detection.

FIG. 8 illustrates different techniques for determining saliency.

FIG. 9 illustrates a generalized flow process for generating an enhancedimage using saliency measures, alpha maps, and edge detection weights.

FIG. 10 illustrates an example equation that may be used to generate analpha map.

FIGS. 11A, 11B, and 11C illustrate example scenarios in which anenhanced image is generated.

FIG. 12 illustrates a flow chart of an example method for generating anenhanced image.

FIGS. 13A and 13B illustrate example processes for generating an alphamap.

FIG. 14 illustrates an example alpha map.

FIG. 15 shows how the alpha map can be fed as input into a hue colorchannel.

FIG. 16 shows a resulting enhanced image that also reflects color, wherethe color reflects an additional property.

FIG. 17 illustrates how the color in the enhanced image can, at somelocations, be blended.

FIG. 18 illustrates a flowchart of an example method for combiningmultiple images into a colorized enhanced image.

FIG. 19 illustrates an example computer system configured to perform anyof the disclosed operations.

DETAILED DESCRIPTION

Embodiments disclosed herein relate to systems, devices (e.g., wearabledevices, head mounted devices, hardware storage devices, etc.), andmethods for generating an enhanced image, where the enhanced imagereflects pixel content obtained from cameras of different modalities aswell as contextual content representative of a particular property.

Some embodiments generate a first image of an environment using a firstcamera of a first modality (e.g., perhaps a thermal camera). A secondimage of the environment is generated using a second camera of a secondmodality (e.g., perhaps a low light camera). The embodiments identifypixels that are common between the two images. An alpha map isgenerated, where the alpha map reflects edge detection weights that havebeen computed for the common pixels based on saliency values. Theembodiments determine how much texture from the images to use togenerate an enhanced image. Based on those weights, textures are mergedfrom the common pixels to generate the enhanced image. Color is alsoadded to the enhanced image, where the color reflects an additionalproperty (e.g., the texture source for the pixel) that is associatedwith one or both of the images.

Some embodiments combine pixel information from multiple differentimages into a single colorized enhanced image. Specifically, someembodiments obtain a first image and a second image of an environment.Those images are then used to generate a colorized enhanced image. Togenerate the colorized enhanced image, the embodiments use pixelinformation for the first image to populate pixel intensity informationfrom the colorized enhanced image. Additionally, the embodiments usepixel information from the second image to determine a huecharacteristic of the colorized enhanced image.

Examples of Technical Benefits, Improvements, and Practical Applications

The following section outlines some example improvements and practicalapplications provided by the disclosed embodiments. It will beappreciated, however, that these are just examples only and that theembodiments are not limited to only these improvements.

The disclosed embodiments bring about substantial benefits to thetechnical field. By way of example, the embodiments are able to produceor generate a so-called “enhanced” image. Different camera modalitiesare designed to provide different types of benefits. By following thedisclosed principles, the embodiments are able to generate an enhancedimage, which enables the benefits that are available to each individualmodality to now be made available via a single image as opposed tomultiple images. In doing so, improved analytics, computer vision, anduser interaction with the computer system are achieved. Furthermore, theuser (in some instances) is provided with content that he/she wouldpotentially not be able to view or interact with if only a single imagetype or modality were used.

In addition to the content provided by the different camera modalities,the embodiments further improve the enhanced image by adding a colorcharacteristic to the enhanced image. This color characteristic isdesigned to represent an additional property that may be of interest toa user of the system. By way of example, the color characteristic orproperty can reflect a texture source as to where a pixel's textureoriginated. Stated differently, the texture for the pixels in theenhanced image come from one or a combination of multiple differentcameras. By adding color, the embodiments are able to visually indicatefrom what source (e.g., the thermal camera, the low light camera, amonochrome camera, etc.) the texture for that pixel came from, or evenwhether the pixel's texture was generated from multiple sources. Indoing so, the embodiments are able to improve the user's experience withthe computer system by providing additional information.

Yet another benefit of performing the disclosed operations is that noinformation is lost. In accordance with the disclosed principles, coloris added to an image to reflect the source of a pixel's texture. Fromthis new and improved image, no information is lost. That is, from thecolorized image, an alpha map can be reverse generated and evenintensity values can also be reconstructed. By following the disclosedprinciples, information is not overwritten and thus is not lost. Yetanother benefit is that the disclosed embodiments preserve essentiallyall spatial information while simultaneously also providing content toreflect the texture source or contributor for a particular pixel.Accordingly, these and other benefits will be described in more detailthroughout the remaining portion of this disclosure.

Example HMDs & Scanning Systems

Attention will now be directed to FIG. 1, which illustrates an exampleof a head-mounted device (HMD) 100. HMD 100 can be any type of MR system100A, including a VR system 100B or an AR system 100C. It should benoted that while a substantial portion of this disclosure is focused onthe use of an HMD to scan an environment to provide a passthroughvisualization (aka passthrough image), the embodiments are not limitedto being practiced using only an HMD. That is, any type of scanningsystem can be used, even systems entirely removed or separate from anHMD. For example, a self-driving car can implement the disclosedoperations.

Consequently, the disclosed principles should be interpreted broadly toencompass any type of scanning scenario or device. Some embodiments mayeven refrain from actively using a scanning device themselves and maysimply use the data generated by the scanning device. For instance, someembodiments may at least be partially practiced in a cloud computingenvironment.

HMD 100 is shown as including scanning sensor(s) 105 (i.e. a type ofscanning or camera system), and HMD 100 can use the scanning sensor(s)105 to scan environments, map environments, capture environmental data,and/or generate any kind of images of the environment (e.g., bygenerating a 3D representation of the environment or by generating a“passthrough” visualization). Scanning sensor(s) 105 may comprise anynumber or any type of scanning devices, without limit.

In accordance with the disclosed embodiments, the HMD 100 may be used togenerate a passthrough visualization of the user's environment. A“passthrough” visualization refers to a visualization that reflects whatthe user would see if the user were not wearing the HMD 100, regardlessof whether the HMD 100 is included as a part of an AR system or a VRsystem, though that passthrough image may be supplemented withadditional or enhanced content. To generate this passthroughvisualization, the HMD 100 may use its scanning sensor(s) 105 to scan,map, or otherwise record its surrounding environment, including anyobjects in the environment, and to pass that data on to the user toview. In many cases, the passed-through data is modified to reflect orto correspond to a perspective of the user's pupils. The perspective maybe determined by any type of eye tracking technique.

To convert a raw image into a passthrough image, the scanning sensor(s)105 typically rely on its cameras (e.g., head tracking cameras, handtracking cameras, depth cameras, or any other type of camera) to obtainone or more raw images of the environment. In addition to generatingpassthrough images, these raw images may also be used to determine depthdata detailing the distance from the sensor to any objects captured bythe raw images (e.g., a z-axis range or measurement). Once these rawimages are obtained, then passthrough images can be generated (e.g., onefor each pupil), and a depth map can also be computed from the depthdata embedded or included within the raw images.

As used herein, a “depth map” details the positional relationship anddepths relative to objects in the environment. Consequently, thepositional arrangement, location, geometries, contours, and depths ofobjects relative to one another can be determined. From the depth maps(and possibly the raw images), a 3D representation of the environmentcan be generated.

Relatedly, from the passthrough visualizations, a user will be able toperceive what is currently in his/her environment without having toremove or reposition the HMD 100. Furthermore, as will be described inmore detail later, the disclosed passthrough visualizations will alsoenhance the user's ability to view objects within his/her environment(e.g., by displaying additional environmental conditions that may nothave been detectable by a human eye).

It should be noted that while the majority of this disclosure focuses ongenerating “a” passthrough image, the embodiments actually generate aseparate passthrough image for each one of the user's eyes. That is, twopassthrough images are typically generated concurrently with oneanother. Therefore, while frequent reference is made to generating whatseems to be a single passthrough image, the embodiments are actuallyable to simultaneously generate multiple passthrough images.

In some embodiments, scanning sensor(s) 105 include visible lightcamera(s) 110, low light camera(s) 115, thermal imaging camera(s) 120,ultraviolet (UV) cameras 125, monochrome 130 cameras, and infraredcamera(s) 135. The ellipsis 140 demonstrates how any other type ofcamera or camera system (e.g., depth cameras, time of flight cameras,etc.) may be included among the scanning sensor(s) 105. In this regard,cameras of different modalities (as reflected by modality 145) areincluded on the HMD 100. The scanning sensor(s) 105 generate images,which may be used to generate passthrough images, which may then bedisplayed on a display 150 of the HMD 100.

In some embodiments, the visible light camera(s) 110 and the low lightcamera(s) 115 (aka low light night vision cameras) operate inapproximately the same overlapping wavelength range. In some cases, thisoverlapping wavelength range is between about 400 nanometers and about1,100 nanometers. Additionally, in some embodiments these two types ofcameras are both silicon detectors.

One distinguishing feature between these two types of cameras is relatedto the illuminance conditions or illuminance range(s) in which theyactively operate. In some cases, the visible light camera(s) 110 are lowpower cameras and operate in environments where the illuminance isbetween about 10 lux and about 100,000 lux, or rather, the illuminancerange begins at about 10 lux and increases beyond 10 lux. In contrast,the low light camera(s) 115 consume more power and operate inenvironments where the illuminance range is between about 110 micro-luxand about 10 lux.

The thermal imaging camera(s) 120, on the other hand, are structured todetect electromagnetic radiation or IR light in the far-IR (i.e.thermal-IR) range, though some embodiments also enable the thermalimaging camera(s) 120 to detect radiation in the mid-IR range. Toclarify, the thermal imaging camera(s) 120 may be a long wave infraredimaging camera structured to detect electromagnetic radiation bymeasuring long wave infrared wavelengths. Often, the thermal imagingcamera(s) 120 detect IR radiation having wavelengths between about 8microns and 14 microns. These wavelengths are also included in the lightspectrum(s). Because the thermal imaging camera(s) 120 detect far-IRradiation, the thermal imaging camera(s) 120 can operate in anyilluminance condition, without restriction.

Accordingly, as used herein, reference to “visible light cameras”(including “head tracking cameras”), are cameras that are primarily usedfor computer vision to perform head tracking. These cameras can detectvisible light, or even a combination of visible and IR light (e.g., arange of IR light, including IR light having a wavelength of about 850nm). In some cases, these cameras are global shutter devices with pixelsbeing about 3 μm in size. Low light cameras, on the other hand, arecameras that are sensitive to visible light and near-IR. These camerasare larger and may have pixels that are about 8 μm in size or larger.These cameras are also sensitive to wavelengths that silicon sensors aresensitive to, which wavelengths are between about 350 nm to 1100 nm.Thermal/long wavelength IR devices (i.e. thermal imaging cameras) havepixel sizes that are about 10 μm or larger and detect heat radiated fromthe environment. These cameras are sensitive to wavelengths in the 8 μmto 14 μm range. Some embodiments also include mid-IR cameras configuredto detect at least mid-IR light. These cameras often comprisenon-silicon materials (e.g., InP or InGaAs) that detect light in the 800nm to 2 μm wavelength range.

Accordingly, the disclosed embodiments may be structured to utilizenumerous different camera modalities. The different camera modalitiesinclude, but are not limited to, visible light or monochrome cameras,low light cameras, thermal imaging cameras, and UV cameras.

It should be noted that any number of cameras may be provided on the HMD100 for each of the different camera types/modalities. That is, thevisible light camera(s) 110 may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,or more than 10 cameras. Often, however, the number of cameras is atleast 2 so the HMD 100 can perform stereoscopic depth matching.Similarly, the low light camera(s) 115, the thermal imaging camera(s)120, the UV camera(s) 125, the monochrome 130 cameras, and the infraredcamera(s) 135 may each respectively include 1, 2, 3, 4, 5, 6, 7, 8, 9,10, or more than 10 corresponding cameras.

FIG. 2 illustrates an example HMD 200, which is representative of theHMD 100 from FIG. 1. HMD 200 is shown as including multiple differentcameras, including cameras 205, 210, 215, 220, and 225. Cameras 205-225are representative of any number or combination of the visible lightcamera(s) 110, the low light camera(s) 115, the thermal imagingcamera(s) 120, the UV camera(s) 125, the monochrome 130 cameras, and theinfrared camera(s) 135 from FIG. 1. While only 5 cameras are illustratedin FIG. 2, HMD 200 may include more or less than 5 cameras.

In some cases, the cameras can be located at specific positions on theHMD 200. For instance, in some cases a first camera (e.g., perhapscamera 220) is disposed on the HMD 200 at a position above a designatedleft eye position of any users who wear the HMD 200 relative to a heightdirection of the HMD. For instance, the camera 220 is positioned abovethe pupil 235. As another example, the first camera (e.g., camera 220)is additionally positioned above the designated left eye positionrelative to a width direction of the HMD. That is, the camera 220 ispositioned not only above the pupil 235 but also in-line relative to thepupil 235. When a VR system is used, a camera may be placed directly infront of the designated left eye position. For example, with referenceto FIG. 2, a camera may be physically disposed on the HMD 200 at aposition in front of the pupil 235 in the z-axis direction.

When a second camera is provided (e.g., perhaps camera 210), the secondcamera may be disposed on the HMD at a position above a designated righteye position of any users who wear the HMD relative to the heightdirection of the HMD. For instance, the camera 210 is above the pupil230. In some cases, the second camera is additionally positioned abovethe designated right eye position relative to the width direction of theHMD. In some cases, the first camera is a low light camera, and the HMDincludes one or more low light cameras. In some cases, the second camerais a thermal imaging camera, and HMD includes one or more thermalimaging cameras. The HMD may additionally include multiple visible lightRGB cameras or monochrome cameras. When a VR system is used, a cameramay be placed directly in front of the designated right eye position.For example, with reference to FIG. 2, a camera may be physicallydisposed on the HMD 200 at a position in front of the pupil 230 in thez-axis direction.

When a user wears HMD 200, HMD 200 fits over the user's head and the HMD200's display is positioned in front of the user's pupils, such as pupil230 and pupil 235. Often, the cameras 205-225 will be physically offsetsome distance from the user's pupils 230 and 235. For instance, theremay be a vertical offset in the HMD height direction (i.e. the “Y”axis), as shown by offset 240. Similarly, there may be a horizontaloffset in the HMD width direction (i.e. the “X” axis), as shown byoffset 245.

As described earlier, HMD 200 is configured to provide passthroughimage(s) 250 for the user of HMD 200 to view. In doing so, HMD 200 isable to provide a visualization of the real world without requiring theuser to remove or reposition HMD 200. Sometimes, the visualization isenhanced, modified, or supplemented with additional information, as willbe described in more detail later. The passthrough image(s) 250effectively represent the same view the user would see if the user werenot wearing HMD 200. Cameras 205-225 are used to provide thesepassthrough image(s) 250.

None of the cameras 205-225, however, are directly aligned with thepupils 230 and 235. The offsets 240 and 245 actually introducedifferences in perspective as between the cameras 205-225 and the pupils230 and 235. These perspective differences are referred to as“parallax.”

Because of the parallax occurring as a result of the offsets 240 and245, raw images produced by the cameras 205-225 are not available forimmediate use as passthrough image(s) 250. Instead, it is beneficial toperform a parallax correction 255 (aka an image synthesis) on the rawimages to transform the perspectives embodied within those raw images tocorrespond to perspectives of the user's pupils 230 and 235. Theparallax correction 255 includes any number of distortion corrections(e.g., to correct for concave or convex wide or narrow angled cameralenses), epipolar transforms (e.g., to parallelize the optical axes ofthe cameras), and/or reprojection transforms (e.g., to reposition theoptical axes so as to be essentially in front of or in-line with theuser's pupils). The parallax correction 255 may include performing depthcomputations to determine the depth of the environment and thenreprojecting images to a determined location or as having a determinedperspective. As used herein, the phrases “parallax correction” and“image synthesis” may be interchanged with one another and may includeperforming stereo passthrough parallax correction and/or imagereprojection parallax correction.

In some cases, the parallax correction 255 includes a planarreprojection 260 where all pixels of an image are reprojected to acommon planar depth. In some cases, the parallax correction 255 includesa full reprojection 265 where various pixels are reprojected todifferent depths.

By performing these different transforms or reprojections, theembodiments are optionally able to perform three-dimensional (3D)geometric transforms on the raw camera images to transform theperspectives of the raw images in a manner so as to correlate with theperspectives of the user's pupils 230 and 235. Additionally, the 3Dgeometric transforms rely on depth computations in which the objects inthe HMD 200's environment are mapped out to determine their depths.Based on these depth computations, the embodiments are able tothree-dimensionally reproject or three-dimensionally warp the raw imagesin such a way so as to preserve the appearance of object depth in thepassthrough image(s) 250, where the preserved object depth substantiallymatches, corresponds, or visualizes the actual depth of objects in thereal world. Accordingly, the degree or amount of the parallax correction255 is at least partially dependent on the degree or amount of theoffsets 240 and 245.

By performing the parallax correction 255, the embodiments effectivelycreate “virtual” cameras having positions that are in front of theuser's pupils 230 and 235. By way of additional clarification, considerthe position of camera 205, which is currently above and to the left ofthe pupil 230. By performing the parallax correction 255, theembodiments programmatically transform images generated by camera 205,or rather the perspectives of those images, so the perspectives appearas though camera 205 were actually positioned immediately in front ofpupil 230. That is, even though camera 205 does not actually move, theembodiments are able to transform images generated by camera 205 sothose images have the appearance as if camera 205 were positioned infront of pupil 230.

Generating Images

Attention will now be directed to FIG. 3, which illustrates anenvironment 300 in which an HMD 305 is operating. Notice, the HMD 305includes at least a first camera 310 and a second camera 315. It isoften the case that the modality of the first camera 310 is differentthan the modality of the second camera 315. For example, the firstcamera 310 may have a thermal image modality while the second camera 315may have a low light modality. Of course, other modalities (such as theones discussed previously) may also be used.

FIG. 3 shows how the first camera 310 is scanning the environment 300,as shown by scan 320. Likewise, the second camera 315 is also scanningthe environment 300, as shown by scan 325. As a result of performing thescans, the first camera 310 generates a first image 330, and the secondcamera 315 generates a second image 335. Of course, any number of imagesmay be generated.

FIG. 4 shows a first image 400, which is representative of the firstimage 330 from FIG. 3. The first image 400 is comprised of a set ofpixels 405, such as pixel 410. A texture may be determined for eachpixel or for at least a group of pixels. For example, the pixel 410 isshown as having a texture 415 (e.g., the texture value “91”).

As used herein, the term “texture” generally refers to a metric or a setof values that quantify the spatial arrangement of intensity and/orcolor of a pixel in an image. Stated differently, texture characterizesthe spatial distribution of a pixel's intensity level relative to thatpixel's neighboring pixels. Texture is also used to divide or partitionan image into different so-called “regions of interest” so those regionscan then be segmented or classified. Additionally, texture reflects orquantifies other characteristics, such as smoothness, coarseness, andregularity.

FIG. 4 shows a second image 420, which is representative of the secondimage 335 of FIG. 3. Notice, the second image 420 is also comprised of aset of pixels 425.

Due to the placement of the cameras on the computer system (e.g., theHMD 305 of FIG. 3) the fields of view (FOV) of the different cameras atleast partially overlap with one another. Consequently, the resultingimages from the first and second cameras include corresponding orsimilar content. The set of common pixels 430 illustrates this concept.That is, the set of common pixels 430 are pixels representing the sameenvironmental content, which has been captured by both the first image400 and the second image 420. Accordingly, the various images includepixels and textures 435.

It should be noted that the texture values for the common pixels betweenthe two images may vary even though they reflect the same area of theenvironment. By way of example, suppose a thermal imaging camera wasdirected toward a spotlight and further suppose a low light camera wasalso directed toward that same spotlight. Here, the two resulting imageswould capture the same area of the environment, but the pixelintensities will likely be different. That is, the illumination providedby the spotlight might possibly saturate the low light camera sensorswhile that same illumination might have no effect on the sensors of thethermal imaging camera. Further details on this aspect will be providedlater. In any event, the embodiments are able to determine which pixelsof which images reflect similar or corresponding content, even if thetexture or intensity values for those pixels are different between thetwo images.

In accordance with the disclosed principles, the embodiments are able toperform edge detection 500, as shown in FIG. 5, on each one of thedifferent images. To clarify, edge detection may be performed on thefirst image 400 and also on the second image 420 shown in FIG. 4. Insome cases, the edge detection operations may be limited or restrainedto only those pixels that are common between the two images (e.g., theset of common pixels 430).

FIG. 5 shows a set of pixels 505A, which are representative of the setof common pixels 430 from FIG. 4. Notice, the set of pixels 505A alsoshows the textures for the various pixels, such as texture 510A and515A.

The embodiments are able to use the various pixels to perform the edgedetection 500. For example, in the set of pixels 505B, which arerepresentative of the set of pixels 505A, notice the stark contrastbetween the texture 510B and the texture 515B. That is, the texturevalue for the texture 510B is “10” while the texture value for thetexture 515B is “87.” The variance or difference between these texturevalues surpasses a threshold value, thereby causing the embodiments toidentify or determine an “edge” exists between the respective pixels.The boldened line between the two pixels reflects this detected edge, asshown by detected edge 520.

As used herein, the term “edge” generally reference to a significantlocal change that exists in an image, such as a change in the image'sintensity at a particular location. An edge can also be thought of as adiscontinuity change in the image's intensity at that particularlocation. The process of detecting edges, or “edge detection,” is thetechnique for identifying points or locations within an image where theimage's intensity or brightness changes dramatically and produces thediscontinuity.

The term “saliency” (such as saliency 525) reflects an amount of texturevariation 530 that exists between groups of pixels. In this case, thetexture variation 530 between texture 510B and 515B is the value “77,”which (in this example case) surpasses a predetermined threshold valuefor determining whether an edge exists between pixels. If the saliencyfor a group of pixels meets or exceeds the threshold value, then an edgeexists.

The embodiments are able to analyze the textures for the various pixelsin the images to determine the texture variations between the pixels.The texture variations are then used to determine the saliency of theimage and to detect the presence or absence of edges between pixels.

Turning briefly to FIG. 6, this figure shows an edge detection 600process performed on an image 605. The result of the edge detection 600is the image 610, which illustrates the various detected edges 615.These edges 615 were detected based on the saliency and texturevariations that were identified by analyzing the image 605.

Edge detection may be performed in a number of ways. One exampletechnique is illustrated in FIG. 7.

FIG. 7 shows an edge detection 700 technique that is based on kernelconvolution 705. Kernel convolution 705 involves the use of a kernel710, which is a defined grouping of pixels or which can also be referredto as a small matrix. In some cases, the grouping of pixels may be a“3×3” 715 group of pixels, a “5×5” 720 group of pixels, or a “7×7” 725group of pixels. Stated differently, various edge detection weights orresponses (aka alpha intensities) may be computed by kernel convolution705, where a kernel 710 of pixels used by the kernel convolution 705 iscomprised of a group of pixels.

In image processing, kernel convolution 705 is generally used for edgedetection, sharpening, blurring, or even embossing. The technique isperforming by executing a convolution between a kernel and an image.Briefly, a convolution is a technique for adding a particular element ofan image to its adjacent neighbors, weighted by the kernel. Accordingly,edges in an image may be detected by performing kernel convolution 705.

Saliency can also be performed using a variety of techniques, asillustrated in FIG. 8 via the saliency determination 800. As shown, thesaliency determination 800 includes, but is not limited to, use of aSobel filter 805 to determine saliency. A Sobel filter 805 is used inimage processing to create an image focused on edges. Generally, theSobel filter 805 is a type of discrete differentiation operator (e.g., asingle derivative filter) that computes the approximation of an imageintensity's gradient.

Optionally, the saliency determination 800 may be based on the use of aLaplacian filter 810. The Laplacian filter 810 is a type of derivativefilter designed to extract both the vertical and horizontal edges froman image, thereby causing the Laplacian filter 810 to be distinct fromthe Sobel filter 805 (i.e. a type of single derivative filter).

Optionally, saliency can be computed based on a computed variance ofintensity values for pixels included within a batch of pixels, as shownby computed variance 815. Optionally, saliency can be determined using aneural network 820 or any type of machine learning. Any type of MLalgorithm, model, machine learning, or neural network may be used toidentify edges. As used herein, reference to “machine learning” or to aML model or to a “neural network” may include any type of machinelearning algorithm or device, neural network (e.g., convolutional neuralnetwork(s), multilayer neural network(s), recursive neural network(s),deep neural network(s), dynamic neural network(s), etc.), decision treemodel(s) (e.g., decision trees, random forests, and gradient boostedtrees), linear regression model(s) or logistic regression model(s),support vector machine(s) (“SVM”), artificial intelligence device(s), orany other type of intelligent computing system. Any amount of trainingdata may be used (and perhaps later refined) to train the machinelearning algorithm to dynamically perform the disclosed operations.

The ellipsis 825 demonstrates how other techniques may also be used todetermine saliency. Accordingly, various different techniques may beused to detects edges and saliencies.

Generating an Enhanced Image

Attention will now be directed to FIG. 9, which illustrates a flow chart900 of an example process involving the use of images, textures,saliencies, and an alpha map to generate an enhanced image. Initially,FIG. 9 shows a first image 905, which corresponding texture 910, and asecond image 915, which corresponding texture 920. Using the principlesdiscussed earlier, the embodiments are able to determine a firstsaliency 925 for the first image 905 and a second saliency 930 for thesecond image 915.

An alpha map 935 is then generated based on the first saliency 925 andthe second saliency 930. The alpha map 935 reflects edge detectionweights 940 that have been computed for each of the common pixels thatare common between the first image 905 and the second image 915, and thecomputation is based on the first saliency 925 and the second saliency930.

Turning briefly to FIG. 10, this figure shows an equation 1000 used togenerate an alpha map 1005, which is representative of the alpha map 935from FIG. 9. Specifically, the equation 1000 is as follows:

Alpha Map=(Saliency(B))/((Saliency(A)+Saliency(B)))

Where “Saliency(B)” is the saliency of a thermal image, or rather, thefirst saliency 925 (i.e. the saliency of the first image 905) and where“Saliency(A)” is the saliency of a low light image, or rather, thesecond saliency 930 (i.e. the saliency of the second image 915).

The alpha map 1005 is then shown as comprising a number of pixels, suchas pixel 1010. Each pixel is assigned its own corresponding alphaintensity 1015 (i.e. an “edge detection weight” such as the edgedetection weights 940 in FIG. 9), which is a number between 0 and 1 andwhich is a number that is generated using the equation 1000. An alphaintensity value of 0 indicates an alpha intensity originating only fromthe first image, and a value of 1 indicates an alpha intensityoriginating only from the second image. An alpha intensity value of 0.5indicates an alpha intensity originating from both the first and secondimage in equal parts.

The alpha intensities, or rather the edge detection weights 940, areused to determine how much texture from each respective image will beused when generating an enhanced image. By way of example, an alphaintensity of 1 (or an edge detection weight of 1) indicates that textureoriginating only from the second image will be used and no texture fromthe first image will be used. Relatedly, an alpha intensity of 0indicates that texture originating only from the first image will beused and no texture from the second image will be used. An alphaintensity of 0.5 means textures from both images will be used equally.

In more detail, let “I” be the image of the first modality (e.g.,modality 1) and “J” be the image of the second modality (e.g., modality2). Image “I” is divided into both a low frequency component (e.g.,“I_1”) and a high frequency component and (“I_h”). The low frequencycomponent (“I_1”) is derived by applying a box filter on “I.”

The high frequency component (“I_h”) is computed by subtracting “I_1”from the original image “I.” In other words, “I_h”=“I”−“I_1.”

In the same manner, the image J is decomposed into low and highfrequency components (e.g., “J_1” and “J_h”, respectively). Two alphamaps are then computed (e.g., “alpha_1” for the low frequency images and“alpha_h” for the high frequency images).

The final fused image “F” (aka an enhanced image) is obtained via thefollowing equation:

F=(1−alpha_1)*I_1+alpha_1*J_1+(1−alpha_h)*I_h+alpha_h*J_h.

Computing alpha_1 and alpha_r is performed by computing image saliency.For computing the low frequency saliency maps, the embodiments ignoreimage details and focus on the dominant image edges. The saliency mapS_I_1 is computed for the low frequency component of image I by runningthe following steps:

As a first step, the embodiments scale down the original image I twice.In other words, the image is scaled from (as one example) a 640×480resolution to a 420×240 resolution and then to a 210×120 resolution.

As a second step, the embodiments apply a Sobel filter on the scaledimage. As a third step, the embodiments apply a Gaussian filter on theSobel image. S_I_1 is obtained by upscaling the filtered image twice.For example, the image is upscaled from a 210×120 resolution to a420×240 resolution to a 640×480 resolution.

The saliency map S_J_1 for the low frequency component of image J iscomputed as above with the only difference being that image J is used asan input to the saliency computation. The alpha map (e.g., alpha map 935from FIG. 9) is then computed as follows (thereby reflecting equation1000 from FIG. 10):

alpha_1=S_J_1/(S_I_1+S_J_1)

For computing the high frequency saliency maps, the embodiments do focuson image details. The saliency maps S_I_h and S_J_h for the highfrequency components of images I and J are computed running the sameprocedure as above without applying image scaling. In other words, onlysteps 2 and 3 are run. Finally, the high frequency alpha map (e.g.,alpha map 935 from FIG. 9) is computed by following the equation listedbelow (thereby reflecting equation 1000 from FIG. 10):

alpha_h=S_J_h/(S_I_h+S_J_h)

In this regard, the alpha map 935 from FIG. 9 may comprise multiplealpha maps. Such alpha maps may include an alpha map for a highfrequency computation and an alpha map for a low frequency computation.The equation for calculating the final fused image “F” may then rely onthese various alpha maps to determine the amount of texture to use fromeach image.

Returning to FIG. 9, the flow chart 900 then illustrates a determinationphase 945 where the embodiments determine how much texture from thefirst image 905 and/or the second image 915 to use to generate anenhanced image 950. Such determinations are based on the edge detectionweights 940 (i.e. the alpha intensities) included in the alpha map 935.FIGS. 11A, 11B, and 11C provide additional clarification relative to thedisclosure presented in FIGS. 9 and 10.

In some implementations, the first image 905 and the second image 915are aligned so that their corresponding perspectives match or coincidewith one another. This alignment process may be performed by theparallax correction processes mentioned earlier. In some cases,alignment may also be performed by matching feature points that arepresented between the two images. A “feature point” is considered apoint of interest that provides a clear contrast, such as a corner or anedge. The embodiments are able to align images by identifying and thenmatching common feature points that are present in both the images.

FIG. 11A shows an example of a first image 1100, which is thermal imagegenerated by a camera having a thermal modality. The various heatsignatures of the objects are represented by the different grey tones inthe image. For example, the first image 1100 shows a human standing on awalk, as shown by thermal content 1105.

FIG. 11B, on the other hand, shows an example of a second image 1110,which is a low light image generated by a camera having a low lightmodality. The various lighting signatures (or infrared (IR) signatures)of the objects are represented by the different grey tones in the image.Notice, a spotlight is attached to the edge of the building and isilluminating an area of the walk where, as was shown in FIG. 11A, ahuman is standing. The illuminated area has saturated the low lightcamera, resulting in the saturated region 1115 of the second image 1110.The human that was previously visible in the first image 1100 and thatwas standing in the illuminated area is now not visible in the secondimage 1110.

In contrast, other features that were not visible in the first image1100 are now visible in the second image 1110. For example, the bushes,labeled as content 1120, 1125, and 1130, are now visible in the lowlight image. Those bushes did not have a heat signature and thus werenot visible in the thermal image (i.e. the first image 1100).

Accordingly, the thermal image beneficially visualizes some content thatmay not be visible in a low light image, and the low light imagebeneficially visualizes some content that may not be visible in thethermal image. In accordance with the disclosed principles, it isdesirable to generate an enhanced image that provides the benefits ofboth the low light image and the thermal image. Stated differently, itis desirable to generate an enhanced image that provides the benefitsfrom different images that were generated from different cameras ofdifferent modalities.

Following the flow chart 900 described in FIG. 9, the embodiments areable to generate the enhanced image 1135 illustrated in FIG. 11C.Notice, the enhanced image 1135 provides the benefits obtained fromusing a thermal image and, simultaneously, provides the benefitsobtained from using a low light image. To illustrate, in the enhancedimage 1135, the content 1140 (i.e. the human) is now visible usingpixels obtained from the thermal image. Similarly, the content 1145,1150, and 1155 (i.e. the bushes) are now visible using pixels obtainedfrom the low light image. Such pixels are determined based on theembodiments' abilities to detect edges, as described earlier, using thetexture values. If a set of edges are visible in one image (e.g.,perhaps the thermal image) but those same set of edges are not visiblein a different image (e.g., perhaps the low light image), it suggeststhat the thermal camera was able to pick up or detect content that wasnot visible by the low light camera, and vice versa.

By way of an additional explanation, the pixels in the alpha mapcorresponding to the illuminated area likely reflected alpha intensitiesequal to or approximating the value 0, meaning that almost all of thetexture used in the enhanced image 1135 for the illuminated area camefrom the thermal image. Similarly, the pixels in the alpha mapcorresponding to the bushes likely reflected alpha intensities equal toor approximating the value 1, meaning that almost all of the textureused in the enhanced image 1135 for the bush areas came from the lowlight image. The textures of the other areas of the enhanced image 1135(e.g., the building) likely came from both the thermal image and the lowlight image, and the proportion of texture used depends on the alphaintensities in the alpha map.

In some cases, a color coding scheme may be used to reflect theorigination of texture for a pixel. For instance, texture obtained fromthe first image can have a particular color hue associated with it whiletexture obtained from the second image can have a different color hueassociated with it. When texture is obtained from both images, then theresulting hue can be the combination of the two hues, and the resultinghue is based on the proportion of texture provided by each image. Eachpixel may also be tagged with metadata to reflect the source of thepixel's texture. Further details on this color coding scheme will beprovided momentarily.

As another example, suppose the embodiments were being used in an indoorenvironment where walls were present. In one example case, suppose a setof hot water pipes were located within the wall. The low light camerawould reflect the walls, but the thermal imaging camera would detect aheat signature. By generating the enhanced image, a user will be able todetect the presence of the hot water pipes even though those would notnormally be visible via the naked eye.

Example Methods

The following discussion now refers to a number of methods and methodacts that may be performed. Although the method acts may be discussed ina certain order or illustrated in a flow chart as occurring in aparticular order, no particular ordering is required unless specificallystated, or required because an act is dependent on another act beingcompleted prior to the act being performed.

Attention will now be directed to FIG. 12, which illustrates a flowchartof an example method 1200 for generating an enhanced image, where theenhanced image reflects pixel content obtained from cameras of differentmodalities as well as contextual content representative of a particularproperty. Here, the contextual content is provided via use of the colorcoding scheme briefly introduced earlier (e.g., use of a different colorhue to reflect a source where a pixel's texture originated). In somecases, the method 1200 may be implemented by a computer system or by aHMD that includes a first camera of a first modality (e.g., a thermalmodality) and a second camera of a second modality (e.g., a low lightmodality). In some cases, one of the first modality or the secondmodality is a short wave infrared (SWIR) modality or, alternatively, anear infrared (NIR) modality.

Optionally, the first modality is selected from a group of modalitiescomprising: a visible light modality, a monochrome modality, a nearinfrared (NIR) modality, a short wave infrared (SWIR) modality, athermal modality, or an ultraviolet (UV) modality. As another option,the second modality is also selected from the group of modalities and isdifferent than the first modality.

Method 1200 involves an act (act 1205) of generating a first image(e.g., first image 1100 of FIG. 11A) of an environment using the firstcamera of the first modality. There is also an act (act 1210) ofgenerating a second image (e.g., second image 1110 of FIG. 11B) of theenvironment using the second camera of the second modality. These twoacts may be performed in parallel with one another or in serial with oneanother. For example, in the case where the acts are performed inserial, act 1205 may occur first and then act 1210 or, alternatively,act 1210 may occur first and then act 1205. Notably, the two images atleast partially reflect the same content or the same area of theenvironment.

In some implementations, parallax correction is performed on one or moreof the first image or the second image. The parallax correction isperformed to align the perspectives embodied within the two images, asdiscussed earlier. The parallax correction can involve performing aplanar reprojection or, alternatively, a full reprojection. In somecases, correcting for parallax results in the perspective of the twoimages being aligned with the person's pupils or, alternatively, withanother desired perspective that is different from the person's pupilperspective.

For example, it may be the case that the second image is parallaxcorrected to conform with the perspective embodied by the first image.Alternatively, it may be the case that the first image is parallaxcorrected to conform with perspective embodied by the second image.Indeed, various different perspectives may be achieved by performingparallax correction. Accordingly, a planar reprojection operation or afull reprojection operation may be performed to account for parallax.

Act 1215 involves identifying pixels that are common between the firstimage and the second image. For example, the set of common pixels 430from FIG. 4 can be identified. Such an operation is beneficial so thatprocessing is performed on relevant content, or rather, on content thatis supposedly the same between the two images. Furthermore, such anoperation is performed in order to offset or account for scenarios suchas the one described earlier, where one image may not include adequatetexture but where the other image's texture can compensate for such adeficiency. As discussed earlier, it may be the case that even thoughthe pixels are supposed to represent the same content, the resultingtextures between the two images may be different.

Act 1220 involves generating an alpha map that reflects edge detectionweights that have been computed for each one of the common pixels basedon a first saliency generated for the first image and a second saliencygenerated for the second image. To perform such an operation, theembodiments determine a first set of textures for the common pixelsincluded in the first image and also determine a second set of texturesfor the common pixels included in the second image. For example, thetextures 435 from FIG. 4 can be determined for both images.

Stated differently, a first saliency of the first image is determined.The first saliency reflects an amount of texture variation in the firstimage. Additionally, a second saliency of the second image isdetermined. The second saliency reflects an amount of texture variationin the second image. For example, the saliency 525 from FIG. 5 may bedetermined for both images or at least for the pixels that are commonbetween the two images. Optionally, the first saliency and the secondsaliency are computed using one or more of: a Sobel filter, a Laplacianfilter, a neural network, or a computation based on intensity variationin a group of pixels.

The alpha map is then generated, where the alpha map reflects edgedetection weights, or “alpha intensities” that have been computed foreach one of the common pixels based on the first saliency and the secondsaliency. FIGS. 13A and 13B illustrate example processes on how thealpha map is generated.

In particular, generating the alpha map includes generating a lowfrequency alpha map by performing the operations recited in process1300A of FIG. 13A. Process 1300A includes an act (act 1305) ofdownscaling the first image twice and an act (act 1310) of downscalingthe second image twice. These acts (and the subsequent ones) may beperformed in parallel or in serial with one another.

After the first image is downscaled twice, there is an act (act 1315) ofapplying a Sobel filter on the first image. After the second image isdownscaled twice, there is an act (act 1320) of applying the Sobelfilter on the second image.

After the Sobel filter has been applied to the first image, there is anact (act 1325) of applying a Gaussian filter on the first image. Afterthe Sobel filter has been applied to the second image, there is an act(act 1330) of applying the Gaussian filter on the second image.

Act 1335 involves generating a first low frequency saliency map byupscaling the first image twice after the Gaussian filter has beenapplied. Act 1340 involves generating a second low frequency saliencymap by upscaling the second image twice after the Gaussian filter hasbeen applied.

Act 1345 then includes generating the low frequency alpha map bydividing the second low frequency saliency map by a sum of the first lowfrequency saliency map and the second low frequency saliency map.

The process of generating the alpha map further includes generating ahigh frequency alpha map by performing the process 1300B outlined inFIG. 13B. Process 1300B may be performed in parallel or in serial withprocess 1300A of FIG. 13A.

Process 1300B includes applying (act 1350) the Sobel filter on the firstimage without first downscaling the first image. Act 1355 involvesapplying the Sobel filter on the second image without first downscalingthe second image. These and the subsequent acts may be performed inparallel or in serial with one another.

After the Sobel filter has been applied to the first image, there is anact (act 1360) of applying the Gaussian filter on the first image togenerate a first high frequency saliency map. After the Sobel filter hasbeen applied to the second image, there is an act (act 1365) of applyingthe Gaussian filter on the second image to generate a second highfrequency saliency map.

Act 1370 then involves generating the high frequency alpha map bydividing the second high frequency saliency map by a sum of the firsthigh frequency saliency map and the second high frequency saliency map.These two alpha maps are then used in the “F” equation mentionedearlier.

FIG. 14 illustrates an example of a completed alpha map 1400. The darkpixels reflect texture or pixel content that is sourced from one camera(e.g., the low light camera) while the white pixels reflect texture orpixel content that is sourced from a different camera (e.g., the thermalcamera). To illustrate, supposing the dark pixels came from a low lightcamera and the white pixels came from a thermal camera, one can comparethe alpha map 1400 against the first image 1100 of FIG. 11A and thesecond image 1110 of FIG. 11B. Notice, for areas where the low lightcamera was saturated, the alpha map 1400 shows white pixels; meaningthat those pixels were sourced from the thermal camera. Similarly, forareas that are black in the alpha map 1400, those pixels were sourcedfrom the low light camera. Some areas of the alpha map may includemerged content; meaning that those pixels were sourced from acombination of both the low light camera and the thermal camera (orother camera modalities). Further details on this aspect will beprovided later.

Returning to FIG. 12, act 1225 then involves determining how muchtexture from the first image and/or from the second image to use togenerate an enhanced image. This determining process is based on theedge detection weights included within the alpha map. With reference toFIGS. 11A and 11B, the texture for the thermal content 1105 is visiblein the first image 1100 but is not visible in the second image 1110.Consequently, the embodiments will select the texture for the thermalcontent 1105 from the first image 1100. Similarly, the texture for thecontent 1120, 1125, and 1130 is visible in the second image 1110 but isnot visible in the first image 1100. Consequently, the embodiments willselect the texture for the content 1120, 1125, and 1130 from the secondimage 1110. The alpha map is used to make these determinations becausethe alpha map indicates or reflects the presence or absence of edges ina pixel via the edge detection weights.

Based on the edge detection weights, act 1230 includes merging texturesfrom the common pixels included in the first image and the second imageto generate the enhanced image. The enhanced image 1135 from FIG. 11C isrepresentative.

Method 1200 also includes an act (act 1235) of adding color to theenhanced image to reflect an additional property that is associated withone or both of the first image or the second image. In some embodiments,the additional property is a property that reflects whether a pixel (orrather, a pixel's texture) in the enhanced image originated from thefirst image, the second image, or a combination of the first image andthe second image or, stated differently, originated from the thermalcamera, the low light camera, or a combination of those two cameras. Aswill be discussed momentarily, in most cases, the alpha map is fed asinput into a hue channel. In some cases, however, the thermal image canbe fed as input into the hue channel (i.e. the thermal intensity valuesare used as the input into the hue channel). In any event, theadditional property, which is indicated via the added color provided bythe hue channel, reflects whether the thermal camera, the low lightcamera, or a combination of the thermal camera and the low light camerasourced texture for said pixel. One will appreciate how the embodimentsare not limited to only thermal and low light cameras; indeed, othercamera modalities can also be used. FIG. 15 provides additional details.

Specifically, the alpha map 1500, which is representative of the alphamap 1400 of FIG. 14 and which is representative of the alpha mapmentioned in method 1200 of FIG. 12, is provided as input into a huechannel 1505 of a chroma 1510 color scheme (e.g., a HSV color model, aHSL color model, etc.). A HSV color model is a hue, saturation, andvalue color model while a HSL color model is a hue, saturation, andlightness color model. Such models generally represent alternativerepresentations of a red, green, blue color scheme. The chroma 1510color scheme includes a hue characteristic, a chroma characteristic, anda value characteristic. The chroma 1510 provides a numericalrepresentation or readout of the various colors.

With the HSV model, in some implementations, one of the first or secondimages is entered as input into the intensity channel of the HSV system,the other one of the first or second images is entered into the huechannel of the HSV system, and the saturation channel is hardcoded to aparticular value (e.g., perhaps 0.6). This process can be used to obtaina color value in the HSV system. Then, some embodiments use a convertfunction to transition from the HSV space to the RGB space. Differentcolor systems (e.g., HSL mentioned above) can also be used, where thosecolor systems have separations between intensities and color tone.Accordingly, intensity values can be fed as input into the “V” value ofthe HSV system. The alpha map or, alternatively, the thermal image (orperhaps even another image) is fed as input into the “H” value of theHSV system as a guide for colorization. A predefined hardcoded value canbe used for the “S” value. If colors from green to red are desired, thena mapping can be used, where the mapping defines a “0” alpha value ascorresponding to green and a “1” alpha value as corresponding to red andany value therebetween moves along the HSV cone.

Selecting the saturation value impacts how colorful the resulting imageis. Values closer to “1” result in highly colorful images while valuescloser to “0” result in less colorful images. It may be the case thathighly colorful images are a distraction. As such, mid-range valuesbetween 0 and 1 are typically selected. As indicated above, a value of0.6 is often selected, though other values can also be used.

It is conceivable that the saturation value might not be hardcoded butrather is also dynamic. Indeed, a third image can be fed as input intothe saturation value, thereby causing the saturation value to also vary.It is also possible that the saturation value is customizable by theuser to reflect the user's preference.

With the HSV color model, the chroma 1510 is shown as being a cone.Generally, the color red falls between the values 0 and 60; the coloryellow falls between the values 61 and 120; the color green fallsbetween the values 121 and 180; the color cyan falls between the values181 and 240; the color blue falls between the values 241 and 300; andthe color magenta falls between the values 301 and 360. Saturationdescribes how much grey is in any particular color and is between 0 and100%. The “value” (or “brightness”) term describes the intensity or thebrightness of the color and ranges between 0 and 100% (0 is black and100% is the brightest version of the color).

In some implementations, the embodiments apply the color red to thethermal camera and apply the color green to the low light camera. Pixelsoriginating solely from the thermal camera are colored in red, andpixels originating solely from the low light camera are colored in green(via the mapping operation mentioned earlier). Pixels that are sourcedfrom both the thermal camera and the low light camera use colors acrossthe spectrum between red and green. Of course, other colors can be used.Accordingly, some embodiments optionally hardcode the hue value or thehue channel 1505 with the alpha map to represent the source of thepixel, as indicated above. Therefore, in some embodiments, the“additional property” mentioned in method 1200 refers to the source orsources of a pixel's texture (i.e. represent which camera or camerassourced the texture information for that pixel). FIG. 16 shows anexample of a resulting image.

FIG. 16 shows an enhanced image 1600, which is an improved version ofthe enhanced image 1135 of FIG. 11C. Notice, enhanced image 1600 showscontent 1605, 1610, 1615, and 1620. Additionally, color has been addedto the enhanced image to reflect an additional property (i.e. thetexture source of a pixel), as shown by color 1625 and color 1630. Asdiscussed earlier, the alpha map is used to identify the source of apixel, and that alpha map is relied on when adding color to the image.

In this simplified example, only pixels that were sourced from thethermal camera are colored. Specifically, the colored areas (color 1625and color 1630) were pixels that were sourced only from the thermalcamera, as shown by the first image 1100 of FIG. 11A and the alpha map1400 of FIG. 14.

In some implementations, the enhanced image 1600 may have all of itspixels colorized. In some implementations, a threshold requirement maybe used when determining which pixels will be colored. For instance, thethreshold may be set so that if the thermal camera contributed at least25% (or some other selected percentage value) to a pixel's texture, thenthat pixel will be colorized. As another example, the threshold may beset so that if the low light camera contributed at least 33% (or someother selected percentage value) to a pixel's texture, then that pixelwill be colorized. Any texture threshold can be used. In some case, nothreshold is used, thereby resulting in the scenario where every pixelis colorized.

In some cases, the embodiments perform object segmentation to identifyan object's type. Color can then also be added for objects of a specificpredefined or selected type. As an example, suppose the embodiments areconfigured to identify (via object segmentation) living animals and/orhumans. The embodiments can be configured to perform the operationsdiscussed above and then selectively apply colorization to any animalsor humans present in the image. The colorization can reflect the sourceof the (animal or human's) pixel's texture. Of course, other types ofobjects can be selected as well.

In some cases, the threshold mentioned earlier can be based on the huecolorization values. For instance, a threshold can be based on a HSVvalue and then filtering can occur based on that threshold.

Some pixels may be sourced only from the thermal camera; some pixels maybe sourced only from the low light camera; and some pixels may besourced from a combination of the thermal camera and the low lightcamera. In scenarios where the sourcing is from the combination ofmultiple cameras, then a blend of colors may occur, as shown in FIG. 17.

FIG. 17 shows an example segment or portion of an image that showsmultiple different colors. Color 1700 reflects how those pixels weresourced from perhaps only the thermal camera. Mixed color 1705 is ablend of colors and shows how those pixels were sourced from acombination of both the thermal camera and the low light camera. Color1710 reflects how those pixels were sourced from perhaps only the lowlight camera.

The color values in the mixed color 1705 will be dependent on the amountof texture contribution the thermal camera and the low light cameraprovided, as detailed by the alpha map (e.g., a value of 0.5 indicatesequal contribution). For instance, if both cameras respectivelycontributed 50% of the texture for a particular pixel, then the mixedcolor 1705 will have a yellowish color. On the other hand, if thethermal camera provided only 10% of the texture for the pixel and thelow light camera provided 90% of the texture, then the mixed color 1705will be mostly green. Relatedly, if the thermal camera provided 90% ofthe texture and the low light camera provided only 10% of the texture,then the mixed color 1705 will be mostly red.

In some cases, the enhanced image is then displayed on a display of theHMD or computer system as a passthrough image. A user can then view thepassthrough image via the display.

In some cases, the enhanced image is further analyzed so that objectrecognition or object segmentation is performed on the enhanced image.For example, in a scenario where the embodiments are used in aself-driving car, the car may have at least two different cameras ofdifferent modalities. The car's system can operate in the mannerdescribed above. When the enhanced image is finally generated, the car'ssystem can then analyze the image to identify objects, such as toperform obstacle avoidance or to ensure the car is traveling in adesired path. Therefore, in some situations, the enhanced image may (ormay not) be displayed and may (or may not) be further analyzed in aneffort to identify objects for obstacle avoidance.

Attention will now be directed to FIG. 18, which illustrates a flowchartof an example method 1800 for combining pixel information from multipledifferent images into a single colorized enhanced image. Method 1800includes an act (act 1805) of obtaining a first image of an environment.Act 1810 involves obtaining a second image of the environment.

In some cases, the first image and the second image are derived from thesame raw image that is generated by a single camera. For instance, anyone of a monochrome camera, low light camera, thermal camera, UV camera,or any other type of camera can be used to generate the raw image. Thefirst image can be a grey scale image, and the second image can be animage that is generated as a result of performing edge detection on thegrey scale image. Various edge detection processes were describedearlier.

In some cases, the first image is generated by a first camera of a firstmodality, and the second image is generated by a second camera of asecond modality. For instance, the first camera can be one of amonochrome camera, a low light camera, a thermal camera, a UV camera, orany other type of camera. The second camera can be a different one fromthe group just listed.

Act 1815 then includes generating a colorized enhanced image. Act 1815includes act 1815A and act 1815B, which may be performed in parallelwith one another.

Act 1815A includes using the pixel information from the first image topopulate pixel intensity information from the colorized enhanced image(e.g., by feeding the information into an intensity channel for theimage). Act 1815B involves using pixel information from the second imageto determine a hue characteristic of the colorized enhanced image (e.g.,by feeding the information into a hue channel for the image). Byperforming these processes, the embodiments are able to use the huecharacteristic to reflect the source of origin for the pixels in theimage. For instance, pixels that were sourced from the second image canbe assigned specific hues while pixels that were not sourced from thesecond image can be assigned either no color or a distinct defaultcolor. Accordingly, some embodiments are configured to provide a firstimage as input into an image's intensity channel and to provide a secondimage as input into the image's hue channel. The resulting image is acolorized enhanced image that visually displays the source of pixelcontent, such as whether a particular pixel was sourced from the secondimage or not.

Example Computer/Computer Systems

Attention will now be directed to FIG. 19 which illustrates an examplecomputer system 1900 that may include and/or be used to perform any ofthe operations described herein. Computer system 1900 may take variousdifferent forms. For example, computer system 1900 may be embodied as atablet 1900A, a desktop or a laptop 1900B, a wearable device (e.g., HMD1900C), a mobile device, a standalone device, or any other device asillustrated by the ellipsis 1900D. Computer system 1900 may also be adistributed system that includes one or more connected computingcomponents/devices that are in communication with computer system 1900.

In its most basic configuration, computer system 1900 includes variousdifferent components. FIG. 19 shows that computer system 1900 includesone or more processor(s) 1905 (aka a “hardware processing unit”) andstorage 1910.

Regarding the processor(s) 1905, it will be appreciated that thefunctionality described herein can be performed, at least in part, byone or more hardware logic components (e.g., the processor(s) 1905). Forexample, and without limitation, illustrative types of hardware logiccomponents/processors that can be used include Field-Programmable GateArrays (“FPGA”), Program-Specific or Application-Specific IntegratedCircuits (“ASIC”), Program-Specific Standard Products (“ASSP”),System-On-A-Chip Systems (“SOC”), Complex Programmable Logic Devices(“CPLD”), Central Processing Units (“CPU”), Graphical Processing Units(“GPU”), or any other type of programmable hardware.

As used herein, the terms “executable module,” “executable component,”“component,” “module,” or “engine” can refer to hardware processingunits or to software objects, routines, or methods that may be executedon computer system 1900. The different components, modules, engines, andservices described herein may be implemented as objects or processorsthat execute on computer system 1900 (e.g. as separate threads).

Storage 1910 may be physical system memory, which may be volatile,non-volatile, or some combination of the two. The term “memory” may alsobe used herein to refer to non-volatile mass storage such as physicalstorage media. If computer system 1900 is distributed, the processing,memory, and/or storage capability may be distributed as well.

Storage 1910 is shown as including executable instructions 1915. Theexecutable instructions 1915 represent instructions that are executableby the processor(s) 1905 of computer system 1900 to perform thedisclosed operations, such as those described in the various methods.

The disclosed embodiments may comprise or utilize a special-purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more processors (such as processor(s) 1905) and systemmemory (such as storage 1910), as discussed in greater detail below.Embodiments also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. Such computer-readable media can be any available media thatcan be accessed by a general-purpose or special-purpose computer system.Computer-readable media that store computer-executable instructions inthe form of data are “physical computer storage media” or a “hardwarestorage device.” Computer-readable media that carry computer-executableinstructions are “transmission media.” Thus, by way of example and notlimitation, the current embodiments can comprise at least two distinctlydifferent kinds of computer-readable media: computer storage media andtransmission media.

Computer storage media (aka “hardware storage device”) arecomputer-readable hardware storage devices, such as RANI, ROM, EEPROM,CD-ROM, solid state drives (“SSD”) that are based on RANI, Flash memory,phase-change memory (“PCM”), or other types of memory, or other opticaldisk storage, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store desired program code meansin the form of computer-executable instructions, data, or datastructures and that can be accessed by a general-purpose orspecial-purpose computer.

Computer system 1900 may also be connected (via a wired or wirelessconnection) to external sensors (e.g., one or more remote cameras) ordevices via a network 1920. For example, computer system 1900 cancommunicate with any number devices or cloud services to obtain orprocess data. In some cases, network 1920 may itself be a cloud network.Furthermore, computer system 1900 may also be connected through one ormore wired or wireless networks 1920 to remote/separate computersystems(s) that are configured to perform any of the processingdescribed with regard to computer system 1900.

A “network,” like network 1920, is defined as one or more data linksand/or data switches that enable the transport of electronic databetween computer systems, modules, and/or other electronic devices. Wheninformation is transferred, or provided, over a network (eitherhardwired, wireless, or a combination of hardwired and wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Computer system 1900 will include one or more communicationchannels that are used to communicate with the network 1920.

Transmissions media include a network that can be used to carry data ordesired program code means in the form of computer-executableinstructions or in the form of data structures. Further, thesecomputer-executable instructions can be accessed by a general-purpose orspecial-purpose computer. Combinations of the above should also beincluded within the scope of computer-readable media.

Upon reaching various computer system components, program code means inthe form of computer-executable instructions or data structures can betransferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a network interface card or“NIC”) and then eventually transferred to computer system RANI and/or toless volatile computer storage media at a computer system. Thus, itshould be understood that computer storage media can be included incomputer system components that also (or even primarily) utilizetransmission media.

Computer-executable (or computer-interpretable) instructions comprise,for example, instructions that cause a general-purpose computer,special-purpose computer, or special-purpose processing device toperform a certain function or group of functions. Thecomputer-executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the embodiments may bepracticed in network computing environments with many types of computersystem configurations, including personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, pagers, routers, switches, and the like. The embodiments may alsobe practiced in distributed system environments where local and remotecomputer systems that are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network each perform tasks (e.g. cloud computing, cloudservices and the like). In a distributed system environment, programmodules may be located in both local and remote memory storage devices.

The present invention may be embodied in other specific forms withoutdeparting from its characteristics. The described embodiments are to beconsidered in all respects only as illustrative and not restrictive. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes which come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

What is claimed is:
 1. A method for generating an enhanced image of anenvironment, where the enhanced image reflects pixel content obtainedfrom cameras of different modalities as well as contextual contentrepresentative of a particular property, said method comprising:generating a first image of an environment using a first camera of afirst modality; generating a second image of the environment using asecond camera of a second modality; identifying pixels that are commonbetween the first image and the second image; generating an alpha mapthat reflects edge detection weights that have been computed for eachone of the common pixels based on a first saliency generated for thefirst image and a second saliency generated for the second image;determining how much texture from the first image and/or from the secondimage to use to generate an enhanced image, said determining being basedon the edge detection weights included within the alpha map; based onthe edge detection weights, merging textures from the common pixelsincluded in the first image and the second image to generate theenhanced image; and adding color to the enhanced image to reflect anadditional property that is associated with one or both of the firstimage or the second image.
 2. The method of claim 1, wherein theadditional property is a property that reflects whether a pixel in theenhanced image originated from the first image.
 3. The method of claim1, wherein the additional property is a property that reflects whether apixel in the enhanced image originated from the first image, the secondimage, or a combination of the first image and the second image.
 4. Themethod of claim 1, wherein the alpha map is computed based on the secondsaliency divided by a sum of the first saliency and the second saliency.5. The method of claim 1, wherein the first modality is selected from agroup of modalities comprising: a visible light modality, a monochromemodality, a near infrared (NIR) modality, a short wave infrared (SWIR)modality, a thermal modality, or an ultraviolet (UV) modality, andwherein the second modality is also selected from the group ofmodalities and is different than the first modality.
 6. The method ofclaim 1, wherein every pixel in the alpha map has an alpha intensitybetween 0 and 1, and wherein a value of 0 indicates an alpha intensityoriginating only from the first image and a value of 1 indicates analpha intensity originating only from the second image.
 7. The method ofclaim 1, wherein the first saliency and the second saliency are computedusing one or more of: a Sobel filter, a Laplacian filter, a neuralnetwork, or a computation based on intensity variation in a group ofpixels.
 8. A head mounted device (HMD) configured to generate anenhanced image of an environment, where the enhanced image reflectspixel content obtained from cameras of different modalities as well ascontextual content representative of a particular property, said HMDcomprising: one or more processors; a first camera of a first modality;a second camera of a second modality; and one or more computer-readablehardware storage devices that store instructions that are executable bythe one or more processors to cause the HMD to at least: generate afirst image of an environment using a first camera of a first modality;generate a second image of the environment using a second camera of asecond modality; identify pixels that are common between the first imageand the second image; generate an alpha map that reflects edge detectionweights that have been computed for each one of the common pixels basedon a first saliency generated for the first image and a second saliencygenerated for the second image; determine how much texture from thefirst image and/or from the second image to use to generate an enhancedimage, said determining being based on the edge detection weightsincluded within the alpha map; based on the edge detection weights,merging textures from the common pixels included in the first image andthe second image to generate the enhanced image; and add color to theenhanced image to reflect an additional property that is associated withone or both of the first image or the second image.
 9. The HMD of claim8, wherein the additional property is a property that reflects whethersaid pixel in the enhanced image originated from the first image, thesecond image, or a combination of the first image and the second image.10. The HMD of claim 8, wherein generating the alpha map includesgenerating a low frequency alpha map by performing the following:downscale the first image twice; downscale the second image twice; afterthe first image is downscaled twice, apply a Sobel filter on the firstimage; after the second image is downscaled twice, apply the Sobelfilter on the second image; after the Sobel filter has been applied tothe first image, apply a Gaussian filter on the first image; after theSobel filter has been applied to the second image, apply the Gaussianfilter on the second image; generate a first low frequency saliency mapby upscaling the first image twice after the Gaussian filter has beenapplied; generate a second low frequency saliency map by upscaling thesecond image twice after the Gaussian filter has been applied; andgenerate the low frequency alpha map by dividing the second lowfrequency saliency map by a sum of the first low frequency saliency mapand the second low frequency saliency map.
 11. The HMD of claim 10,wherein generating the alpha map includes generating a high frequencyalpha map by performing the following: apply the Sobel filter on thefirst image without first downscaling the first image; apply the Sobelfilter on the second image without first downscaling the second image;after the Sobel filter has been applied to the first image, apply theGaussian filter on the first image to generate a first high frequencysaliency map; after the Sobel filter has been applied to the secondimage, apply the Gaussian filter on the second image to generate asecond high frequency saliency map; generate the high frequency alphamap by dividing the second high frequency saliency map by a sum of thefirst high frequency saliency map and the second high frequency saliencymap.
 12. The HMD of claim 8, wherein a Sobel filter is used to determinethe first and second saliencies.
 13. The HMD of claim 8, wherein aLaplacian filter is used to determine the first and second saliencies.14. The HMD of claim 8, wherein the first and second saliencies arecomputed based on a computed variance of intensity values for pixelsincluded within a batch of pixels.
 15. The HMD of claim 8, wherein thefirst and second saliencies are determined using a neural network.
 16. Amethod for combining pixel information from multiple different imagesinto a single colorized enhanced image, said method comprising:obtaining a first image of an environment; obtaining a second image ofthe environment; generating a colorized enhanced image by: using pixelinformation from the first image to populate pixel intensity informationfor the colorized enhanced image; and using pixel information from thesecond image to determine a hue characteristic of the colorized enhancedimage.
 17. The method of claim 16, wherein the first image and thesecond image are derived from a same raw image generated by a singlecamera.
 18. The method of claim 17, wherein the first image is a greyscale image and the second image is an image generated as a result ofperforming edge detection on the grey scale image.
 19. The method ofclaim 16, wherein the first image is generated by a first camera, andthe second image is generated by a second camera.
 20. The method ofclaim 19, wherein the first camera is of a first modality, and thesecond camera is of a second modality.